Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework

• Published in: Frontiers in human neuroscience Vol. 12; p. 505
• Main Authors: Pinti, Paola, Scholkmann, Felix, Hamilton, Antonia, Burgess, Paul, Tachtsidis, Ilias
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 11.01.2019; Frontiers Media S.A
• Subjects: Blood pressure; Brain research; Cognitive ability; Design; digital filter; functional data analysis; functional near infrared spectroscopy; general linear model; Heart rate; Hemodynamics; Information processing; Infrared spectroscopy; Medical imaging; Nervous system; Neuroimaging; Neuroscience; Neurosciences; NMR; Nuclear magnetic resonance; Optics; Physiology; pre-processing guidelines; pre-processing standardization; Principal components analysis; Sensors; Spectrum analysis; Standardization; University colleges; United Kingdom > UK; functional data analysis; functional near infrared spectroscopy; digital filter; pre-processing standardization; general linear model; pre-processing guidelines
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2018.00505

Functional near-infrared spectroscopy (fNIRS) research articles show a large heterogeneity in the analysis approaches and pre-processing procedures. Additionally, there is often a lack of a complete description of the methods applied, necessary for study replication or for results comparison. The aims of this paper were (i) to review and investigate which information is generally included in published fNIRS papers, and (ii) to define a signal pre-processing procedure to set a common ground for standardization guidelines. To this goal, we have reviewed 110 fNIRS articles published in 2016 in the field of cognitive neuroscience, and performed a simulation analysis with synthetic fNIRS data to optimize the signal filtering step before applying the GLM method for statistical inference. Our results highlight the fact that many papers lack important information, and there is a large variability in the filtering methods used. Our simulations demonstrated that the optimal approach to remove noise and recover the hemodynamic response from fNIRS data in a GLM framework is to use a 1000th order band-pass Finite Impulse Response filter. Based on these results, we give preliminary recommendations as to the first step toward improving the analysis of fNIRS data and dissemination of the results.